Shape modeling is a challenging task with many potential applications in computer vision and medical imaging. There are many shape modeling methods in the literature, each with its advantages and applications. However, many shape modeling methods have difficulties handling shapes that have missing pieces or outliers. In this regard, this paper introduces shape denoising, a fundamental problem in shape modeling that lies at the core of many computer vision and medical imaging applications and has not received enough attention in the literature. The paper introduces six types of noise that can be used to perturb shapes as well as an objective measure for the noise level and for comparing methods on their shape denoising capabilities. Finally, the paper evaluates seven methods capable of accomplishing this task, of which six are based on deep learning, including some generative models.

翻译：形状建模是一项具有挑战性的任务，在计算机视觉和医学成像领域具有众多潜在应用。文献中存在多种形状建模方法，各具优势与适用场景。然而，许多形状建模方法在处理存在缺失部分或异常值的形状时面临困难。针对这一问题，本文提出形状去噪这一形状建模中的基本问题——该问题居于众多计算机视觉与医学成像应用的核心位置，但尚未在文献中获得足够关注。本文定义了六种可用于扰动形状的噪声类型，并给出了衡量噪声水平以及评估各方法形状去噪能力的客观指标。最后，本文评估了七种能够完成该任务的方法，其中六种基于深度学习，包括部分生成式模型。